Integer linear programming approach to learning Bayesian network
structure: towards the essential graph
In Proceedings of the 6th
European Workshop on Probabilistic Graphical Models
(A. Cano, M. Gomez-Olmedo, T. D. Nielsen eds.),
University of Granada 2012, pp. 307-314.
The basic idea of a geometric approach to learning a Bayesian network (BN)
structure is to represent every BN structure by a certain (uniquely determined)
vector. If the vector representative is chosen properly, it allows one to re-formulate
the task of finding the global maximum of a score over BN structures as an integer linear programming (ILP) problem.
Suitable such a zero-one vector representative is the characteristic imset,
introduced in (Studeny, Hemmecke, Lindner 2010).
In this paper, extensions of characteristic imsets are considered
which additionally encode chain graphs without flags equivalent to acyclic
directed graphs. The main contribution is the polyhedral description (= in terms of a set of linear inequalities)
of the respective domain of the ILP problem. It is just a theoretical result, but it opens the way to
the application of ILP software packages in the area of learning a BN structure.
The advantage of this approach is that, as a by-product of the ILP optimization procedure, one may get the
essential graph, which is a traditional graphical BN representative.
- AMS classification 68T30
- characteristic imset
- integer linear programming
- essential graph
scanned pdf copy (408kB) is available. A substantially extended version of this
conference paper was later published as a journal paper:
The contribution follows the approach proposed in a previous PGM 2010 conference paper:
The technical proof of the main observations are in a research report: